dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The work described in this project was partially funded by the  European  project  ACCOMPANY  ( Acceptable robotics COMPanions for AgeiNg Years). Grant agreement no. :  287624. The authors are grateful to colleagues in the ACCOMPANY consortium (The  University of Hertfordshire, United Kingdom; Hogeschool Zuyd, The Netherlands; Fraunhofer, Germany; University of Amsterdam, The Netherlands; University of Sienna, Italy; Maintien en Autonomie à Domicile des Personnes Agées, France; and University of Birmingham, United Kingdom; University of Twente, the Netherlands; University of Warwick, United Kingdom ).
PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3' IVT Affymetrix chips is available in tabular form at http://bns.
Introduction Since its introduction in the 90's, laboratories and companies involved in the development of the microarray technology have produced many different types of microarray platforms. Affymetrix is the most frequently referenced platform and has designed several chip models to quantify the expression level of transcripts by probing their 3' end (3'-IVT format). These chips show a complicated relationship between probes and genes. Specifically, probes are redundant because in every chip layout a gene is always targeted by a group of 11 or 16 different probes that form a probe set. Probe sets also are redundant as a significant portion of probed genes are targeted by several probe sets. This is the consequence of using full length transcripts and/or expressed sequence tags (EST) to design probe sets.
As several groups have developed their own cdf files in addition to the official ones delivered by Affymetrix, users can encounter difficulties in selecting the best fitting cdf file for their needs. Another problem is the difficulty of using the results delivered by modified cdf files. For example, some specialized software that uses the Affymetrix probe set names in the entry might not recognize proprietary probe set names and thus not allow comparing results obtained with different cdf files. Finally, for those interested in massive analysis that relies on files stored in repositories, such as Gene Expression Omnibus (GEO, http://www. ncbi. nlm. nih. gov/geo/) or ExpressArray (http://www. ebi. ac. uk/arrayexpress/), it would be impossible to use results that do not come with the raw files to recalculate signals with modified cdf files.
We called this method PSAWN (for Probe Set Assignment With Networks) and its development resulted in a program composed of two parts. One developed in Python (PSAWNpy) recovers and organizes all the probe set information by interrogation of the Ensembl (http:// www. ensembl. org/index. xhtml) and AceView (http:// www. ncbi. nlm. nih. gov/IEB/Research/Acembly/ [15] ) databases. The other was developed in Matlab (PSAWNml) to find complex relationships between probe sets and genes and allows estimating the level of similarity of each existing pair of alternative probe sets. Both programs' output results in tab-separated text files. We applied these programs to all the available human, mouse and rat 3'-IVT Affymetrix chips where it was possible to construct more than 10 networks and thus created a new resource that allows users to download description files generated by these programs and that correspond to the most popular Affymetrix chips (http://bns.
Recovering probe and probe set information PSAWNpy is a Python program which allows importing users' data registered in tabular format as well as public data stored in the Ensembl (release 62, April 2011) and AceView databases into local Berkeley databases as explained in the corresponding tutorial (Additional file 1: Figure  S1 -Figure  S10 ). Data processing allows finding correspondences between probes and genes and to generate text tables that list the probe sets which target a given gene with at least one probe. These tables also display other information about the targeting probes, targeted and not targeted known transcripts and probe location (in exons, introns, and upstream or downstream of a gene within a limit of 2 kb) (Additional file 1: Figure  S12  and S13 ). As some probes were not located in known genes, we created a special category called GOP (group of probes), in which the interval between a probe and its direct neighbour is less or equal to 2 kb.
Class and gene assignment of probe sets -similarity of alternative probe sets Data generated by PSAWNpy were processed by PSA WNml, a Matlab program that constructs biclusters (groups containing two types of entities) of probe sets and genes by applying a clustering algorithm and assigns them to different classes, according to the complexity of the relationship between the number of targeted genes and of targeting probe sets. PSAWNml assigns each probe set to a single gene. When several genes are targeted, PSAWNml selects successively genes with the following characteristics until only one gene is left: the highest number of targeting probes, the best target type (probes in exons > probes in introns), the maximal ratio between number of targeting probe sets and number of groups of probe sets targeting the same transcript(s), the minimal number of targeting probe sets, the gene source (Ensembl > AceView > GOP) and finally the first gene in alphabetic order.
Comparison with the results by Elbez and co-workers In order to compare our results with those already published, we constructed two sets of alternative probe sets that could be assimilated to the 'good' and 'bad' categories defined by Elbez and colleagues  [7]. More precisely, we considered as good all the alternative probe sets that had 100% similarity and as bad all the alternative probe sets with 0% similarity. Elbez et al. defined a third category that grouped all non-informative pairs (NI in Table  6 ). These pairs have at least one probe set which is considered as absent or with a fold change (defined as the ratio between its signal and the mean signal calculated for the whole dataset) higher than 2 in less than 10% of the experimental conditions in all datasets. Using our approach we did not detect this kind of noninformative probe sets and thus we grouped all alternative probe sets with intermediate similarity (greater than 0% and smaller than 100%) in an 'intermediate' category (Inter in Table  6 ).
Distribution of positively and negatively correlated pairs of alternative probe sets in a large series of comparisons Recently, several algorithms have been developed to find alternative probe sets that are differentially regulated in two biological conditions and some have been tested in specific models, for instance to characterize isoform-specific degradation during oocyte maturation  [12] , or disturbance of alternative splicing regulation following transfection with oncogenes  [13]. This prompted us to study the similarity distribution of positively and negatively correlated pairs of alternative probe sets in a large series of comparisons. For example, for chip m3, in our database there are 748 biological conditions that we used to construct the corresponding networks. More specifically, all possible comparisons (748*747/2 = 279378) were analysed by applying the RDAM algorithm  [16]  and the networks constructed by selecting appropriate subsets of these comparisons.
For the analysis we set up a new classification of probe sets relative to the number of genes they target, and showed that these classes behave differently in MCL clustering. Our work has led to the development of a complete set of open source tools in Python and Matlab that allow the complete analysis of the probe set characteristics within transcriptional networks. The software we developed delivers a full textual description of each probe set (which genes, exons and transcripts are targeted by a given number of probes) as well as information on the number and properties of secondary targets (Additional file 1: Figures  S12, S13 , S14, S15 and Figure  S54 ). The software outputs another file which gives the similarity of each pair of alternative probe sets and the probability that the pair is positively or negatively correlated in a huge series of comparisons between two biological comparisons (observed probabilities).
Many Affymetrix users carry out simple analyses mostly to detect the more varying genes. Users interested in 3' alternative poly-adenylation (the main alternative type of transcripts targeted by 3-IVT chips) will find in the tables we generated the information on alternative probe sets that target the genes which are in their top list. For instance, if one alternative probe set is upregulated and the other down-regulated, the level of similarity and the percentage of positive and negative correlations indicate whether this event is unexpected and deserves more investigation. Finally these data and these programs could be used to solve related questions. As already mentioned, the fact that the rank difference between two alternative probe sets is correlated with their similarity (Figure  5 ) is a strong indication that it could be possible to determine whether they target the same group of transcripts in a given biological condition.
Acknowledgements We thank  Azzeddine Ghers  and  Nicolas Rouquette  for their participation in preliminary works concerning this project. We acknowledge  E. Andermarcher  and  M. C. Morris  for proofreading the manuscript and  Hendrik Luuk  for his comments. Computing needed by this project was carried out using HPC resources from  GENCI-CINES  (http://www. cines. fr/)[grant number  c2010075038 ]. Received: 13 February 2013 Accepted: 28 February 2013 Published: 21 March 2013.
According to some authors, episodic memory impairment may be a feature shared by all schizophrenic patients, whereas others argue in favor of the mnesic heterogeneity. Our aims were to determine whether patients can be grouped based on according to their mnesic performances. The California Verbal Learning Test (CVLT), an episodic verbal learning test, was compared in 61 schizophrenic patients and 61 matched healthy subjects. The 32 indices were calculated using CVLT Scoring Software. This process allowed us to describe patients' episodic processes in detail (encoding, storage, retrieval). We isolated one group with normative data, another showed impairment of both encoding and retrieval processes, and in the last one, only encoding process was impaired. As schizophrenia is heterogeneous with regard to episodic memory, impairments should not be considered as a common core to the various forms of the illness and it would be fruitful to systematically assess episodic processes in detail to take into account individual abilities and challenges.
Neuropsychological Evaluation The results of the CVLT allow 32 indices to be calculated to produce a detailed analysis of episodic memory capacity  [20, 24]  (Table  1 ). It is possible to use the paper/pencil way but the analyses take a long time and we are not able to exclude possible miscalculations. Thus, the appropriate instrument for this purpose was the CVLT Scoring Software which was developed by our research group using Access'97 and based on the first CVLT version. This software allows us to calculate all the indices automatically, except the learning slope (which is calculated in Excel). The Weschler Adult Intelligence Scale (WAIS-R,  [25]  was used to provide an estimate of Intelligence Quotient (IQ). Recognition measures  [9] [10] [11] ; Recall errors  [12] [13] [14] [15] ; Recognition errors  [16] [17] [18] [19] [20] ; Organizational strategies  [21] [22] [23] [24] [25] [26] ; Contrast measures  [27] [28] [29] [30] [31] [32].
This is because the traffic (X 3 ) on the pavement whose maintenance is to be optimized depends on the performance of parallel roads (linking the same  locations as the road to which the pavement under study belongs). a θ n 1 a θ n 2 a θ n 3 a θ n 4 a θ n 5 θ n 1 1 0 0 0 0 θ n 2 0 1 0 0 0 θ n 3 0 0 1 0 0 θ n 4 0 0 0 1 0 θ n 5 0 0 0 0 1 If it is not possible to directly observe the value of X 4 , it can be inferred by using conditional probability distributions on the observed values of the variables X 2 (weather conditions) and X 5 (urban growth). The latter also has an effect on X 3. Knowing the state of a pavement with the same type (related to durability) as that of the pavement under study allows inferences to be made about the uncertain state of the latter. The results were computed by using specialized graphical user interface (GUI) software for GPOMDP that was developed by the authors.
Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines, such as urban planning, environmental sciences, and health care. Thus, remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features, and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result, we propose a high level conceptual framework, based on existing and standardized approaches, offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge.
g. , mangrove is located between ocean and www. josis. org continent in a field; vegetal segment is between water and mineral objects in an image). This consideration also applies to temporal characteristics where, for example, information concerning periods in the life of a feature can be seen both in the image (time series) and field (life cycle). Because, spatiotemporal concepts exist in both viewpoints, it seems useful to use them as a common basis for describing the different viewpoints. Thus, we must also take into account the spatiotemporal dimension in the modeling of knowledge. Some progress has been made to model information with spatial and/or temporal dimensions. However, these approaches were not designed especially to track spatiotemporal phenomena  [5] , as they are oriented towards only one particular phenomenon  [3] , or they have been designed for other tools, such as GIS or spatial databases  [17, 33].
Otherwise, the GIS community has been very active in modeling spatiotemporal knowledge for many years  [1, 8, 12, 14, 26, 33]. Some of the work has resulted in standard specifications and recommendations from OGC and ISO  [24, 31] , and has provided design foundations for both GIS software and spatial databases. This expertise would provide invaluable support for the field of image interpretation, so in this paper we rely on these works to formalize the spatiotemporal knowledge used in remote sensing applications. Nevertheless, it is first necessary to represent knowledge in a common formalism.
Metamodel structure A general view of our metamodel is presented in Figure  1 , where eight components have been identified to define spatiotemporal knowledge in the field of image interpretation. We organize them as UML packages in order to aggregate information semantically close and to ensure modularity. We use the merge directed relationship between the CorePackage and the Spa-tialDimensionPackage, the TemporalDimensionPackage, and the SemanticDimensionPackage to indicate that the content of the CorePackage can be extended by the elements of the another packages. In another way, we use the generalization relationship between the diverse packages to express that a relationship must be refined in terms of whether it is spatial, temporal, spatiotemporal, or semantic.
The Core package The CorePackage is the central element of the metamodel and is linked to other packages by UML dependency relationships (Figure  2 ). It is a shared opinion that a geographic feature is an object, which represents an abstraction of a real world phenomenon with a local position from the earth  [24, 31, 33]. Thus a geographic feature has a spatial dimension and characteristics defined by attributes. However, modeling geographic features to track spatiotemporal phenomena, such as the www. josis. org decrease of mangrove forest, involves taking into account both spatial and temporal dimensions  [15, 34]. Moreover, modeling relationships between geographic features can be helpful in the interpretation process to find distinct objects in an image (e. g. , to distinguish that ONE kind of vegetation can be classified as mangrove by knowing that mangrove is currently located between continent and ocean).
Spatial dimension The SpatialDimensionPackage contains information about spatial references of the feature (Figure  3 ). Usually, the spatial dimension of a feature is defined by a location and a geometric shape  [24, 33]. The feature position is provided by geographic or planar coordinates or by an approximation, such as a bounding box. However, whatever the representation mode, it is necessary to add the associated geodesic reference system. Following the point-set topological theory defined by  [12] , many attempts have been made to specify the geometry of a geographic feature, which have been included in the standards (e. g. , ISO19107, ISO 19125-1, OGC Features)  [24, 25, 31]. Thus, we add a class NormalizedShape to our metamodel to set the concepts defined in the OGC and ISO standards.
However, our approach allows us to describe the shape by concepts taken from the application domain through the class Oth-erShape. Indeed, in a satellite image, we can extract the feature shape concepts with some classes defined by remote sensing software (e. g. , Ecognition or OrpheoToolBox). Finally, spatial referencing can be made by using a geographic identifier defined by the Geographi-cIdentifier class in our conceptualization. Indeed, it is useful to be able to denote a feature (e. g. , "Cayenne's coastal" in the field point of view) which can be transformed in a bounding box that delineates the affected area. However, to reduce the semantic gap induced by the fact that the same feature can be named differently, we intentionally constrain the use of geographic identifiers defined in the ISO 19112 standard  [24] , or by a geographical concept contained in the Geoname database.
Temporal dimension The TemporalDimensionPackage includes concepts that characterize time (Figure  4 ). In the literature, there are two ways to describe temporality: talking about time, or modeling the change  [8, 26, 27, 33, 38]. [26]  argues that the temporal dimension is topologically similar to spatial dimension. Following this line of thinking, the ISO19108 standard  [24]  considers time as a dimension, by analogy to the spatial concepts defined in ISO19107. Based on this approach we define three classes, each one able to describe geographic features by the temporal dimension. The ISO19108 class allows the use of concepts that are defined in the standard, such as TM Object. The LifeSpan class is dedicated to associate terminological information with a geographic feature, such as creation or evolution.
Semantic dimension The aim of the SemanticDimensionPackage is to describe other characteristics of a feature, such those of image or landscape properties (Figure  5 ). The SemanticDimensionPackage is defined by a set of concepts that are relevant for a domain of study, for example, the physical properties of an image (e. g. , spectral band, texture) or the description of a landscape (e. g. , Amazonian biome). This package serves to define explicitly the distinct properties of a feature in the different viewpoints (image and field) and thus, can only be specified when the application domain is known, in the model derived from the metamodel.
Relations The RelationPackage contains all the required concepts for describing a relationship between features. It is directly linked to the CorePackage by the Relation association class. The Relation class is an abstract class that must be specialized into four sub-classes in order to refine the relationship in terms of spatial, temporal, spatiotemporal, and semantic relationships. Additionally, we provide three methods of definition to specify each type of relationship according to the viewpoint taken: (1) MeasurableMethod are methods that define relationships with numerical values (measured or calculated), such as those currently used in standards; (2) QualitativeMethod are methods used to define terms given by the expert to describe relationships; and (3) FuzzyMethod are instantiations of relations defined by the two previous methods on a [0, 1] interval.
[20]  supplements this work by proposing a set of relationships which is a generalized vision of existing spatiotemporal reasoning models. Based on this set,  [19]  has developed a model called life and motion configurations, which is used to formalize the relationships between two spatiotemporal histories. These configurations are generalized into a set of 25 relationships representing spatiotemporal information with a high level of abstraction. These can be expressed in natural language, such as "object A and object B meet during their coexistence," "A is there when B is born and dies," "A and B never meet," and so forth. In another way,  [16]  proposes a model that combines the RCC8 topological relationships with the Allen temporal relationships. According to this previous work, we define a spatiotemporal relationship by a class composed with one or more temporal relationship and one or more spatial relationship, which have each been defined in the SpatialRelationPackage and the TemporalRelation-Package respectively.
Image point of view At the opposite of the field point of view, we also need to describe the representations of field entities in the image. We achieve this by applying an object-based image analysis (using the Orfeo Toolbox software). On the one hand, the result of image computation is described using the reference conceptualization  [13]. The reference conceptualization is formalized knowledge (an www. josis. org ontology) about basic concepts accepted by the community of a domain. Those concepts are relatively independent of any kind of application. The reference conceptualization is also used to express expert concepts in remote sensing contextual knowledge  [13]. This knowledge contains concepts depending on the context of the application. The expert knowledge is defined in terms of image characteristics (radiometric indexes, textures, shape etc.
Additional imaging In addition to the NB118 and J-band VISTA data obtained in this programme, we used Y and J-band VISTA data from the UltraVISTA DR1 dataset  (McCracken et al. 2012). Specifically, for Y we directly use the stack and weight map from  McCracken et al. (2012) , although we add 0. 04 mag to the zeropoint to reproduce the effect of the latest photometric calibration (colour equation) from CASU (Sect. 3. 2). For J, we use the individual images and weight maps  (which McCracken et al. 2012  used to make their stack) and combine them with the individual images and weight maps from our programme  (Sect. 3. 4). This combined J-band stack has a typical exposure time per pixel of 17. 2 h in the stripes of interest here, of which 13. 8 h come from UltraVISTA (Table  2  in  McCracken et al.