dataesr/scientific-paragraphs-categorization huggingface dataset:
Definition of the Mapping Language from RDB2RDF Automatic mappings provided by tools such as d2r 13 and Virtuoso rdf Views provide a good starting point especially in cases when there is no existing Domain Ontology to map the relational schema to. However, most commonly the manual definition of the mappings is necessary to allow users to declare domainsemantics in the mapping configuration and take advantage of the integration 11 http://www. w3. org/2011/10/rdb2rdf-charter. html 12 http://www. w3. org/DesignIssues/RDB-RDF. html 13 http://d2rq. org/d2r-server and linking facilities of Linked Data. r2rml 14  , a w3c recommendation language for expressing such customized mappings, is supported from several tools including Virtuoso rdf Views and d2r.
Materializing the Data A common feature of rdb2rdf tools is the ability to create a "semantic view" of the contents of the relational database. In these cases, an rdf version of the database is produced so that content can be provided through a sparql endpoint and a Linked Data interface that works directly on top of the source relational database, creating a virtual "view" of the database. Such a "semantic view" guarantees up-to-date access to the source business data, which is particularly important when the data is frequently updated. In contrast, generating and storing rdf requires synchronization whenever either the source data model, the target rdf model, or the mapping logic between them changes. However, if business decisions and planning require running complicated graph queries, maintaining a separate rdf store becomes more competitive and should be taken under consideration.
Publishing Pattern for XML Data When the original data is in xml format an xslt transformation to transform the xml document into a set of rdf triples is the appropriate solution. The original files will not change; rather a new document is created based on the content of the existing one. The basic idea is that specific structures are recognized and they are transformed into triples with a certain resource, predicate and value. The LOD2 stack supports xml to rdf/xml xslt transformations. The resulting triples are saved as an rdf/xml graph/file that can follow the same hosting and serving procedures explained in the previous section.
Publishing Pattern for Unstructured Data Despite the evolution of complex storage facilities, the enterprise environment is still a major repository paradigm for unstructured and semi-structured content. Basic corporate information and knowledge is stored in a variety of formats such as pdf, text files, e-mails, classic or semantic annotated websites, may come from Web 2. 0 applications like social networks or may need to be acquired from specific web API's like Geonames 15  , Freebase 16  etc. Linked Data extraction and instance data generation tools maps the extracted data to appropriate ontologies en route to produce rdf data and facilitate the consolidation of enterprise information. A prominent example of a tool from the LOD2 stack that facilitate the transformation of such types of data to rdf graphs is Virtuoso Sponger.
Virtuoso Sponger 17  is a Linked Data middleware that generates Linked Data from a big variety of non-structured formats. Its basic functionality is based on Cartridges, that each one provides data extraction from various data source and mapping capabilities to existing ontologies. The data sources can be in rdfa format 18  , GRDDL 19  , Microsoft Documents, and Microformats 20  or can be specific vendor data sources and others provided by API's. The Cartridges are highly customizable so to enable generation of structured Linked Data from virtually any resource type, rather than limiting users to resource types supported by the default Sponger Cartridge collection bundled as part of the Virtuoso Sponger. The PoolParty Thesaurus Server 21  is used to create thesauri and other controlled vocabularies and offers the possibility to instantly publish them and display their concepts as html while additionally providing machine-readable rdf versions via content negotiation.
This means that anyone using PoolParty can become a w3c standards compliant Linked Data publisher without having to know anything about Semantic Web technicalities. The design of all pages on the Linked Data front-end can be controlled by the developer who can use his own style sheets and create views on the data with velocity templates. DBpedia Spotlight 22  is a tool for automatically annotating mentions of DBpedia resources in text, providing a solution for linking unstructured information sources to the Linked Open Data cloud through DBpedia. DBpedia Spotlight recognizes that names of concepts or entities have been mentioned. Besides common entity classes such as people, locations and organisations, DBpedia Spotlight also spots concepts from any of the 320 classes in the DBpedia Ontology.
The tool currently specializes in English language, the support for other languages is currently being tested, and it is provided as an open source web service. Stanbol 23  is another tool for extracting information from CMS or other web application with the use of a Restful API and represents it as rdf. Both Dbpedia Spotlight and Stanbol support NIF implementation (NIF will soon become a w3c recommendation) to standardise the output rdf aiming on achieving interoperability between Natural Language Processing (NLP) tools, language resources and annotations.
Hosting and Serving The publishing phase usually involves the following steps: 1. storing the data in a Triple Store, 2. make them available from a sparql endpoint, 3. make their uris dereferenceable so that people and machines can look them up though the Web, and 4. provide them as an rdf dump so that data can easily be re-used. The first three steps can be fully addressed with a LOD2 stack component called Virtuoso, while uploading the rdf file to CKAN 24  would be the procedure to make the rdf public. OpenLink Virtuoso Universal Server is a hybrid architecture that can run as storage for multiple data models, such as relational data, rdf, xml, and text documents. Virtuoso supports a repository management interface and faceted browsing of the data. It can run as a Web Document server, Linked Data server and Web Application server.
The open source version of Virtuoso is included in the LOD2 stack and is widely used for uploading data in its Quad store, it According to the fourth step, sharing the data in a well-known open datahub such as CKAN will facilitate their discovery from other businesses and data publishers. The functionality of CKAN is based on packages where data sets can be uploaded. CKAN enables also updates, keeps track of changes, versions and author information. It is advised as good practice to accompany the data sets with information files (e. g. VOID file) that contain relevant metadata (Figs. 9, 10). UK Companies House 27  that contain the metadata description of other companies. The discovery of more related business data can further be facilitated with Linked Data browsers and search engines like SigmaEE 28.
However, the implementation of interlinking between different data sources is not always a straightforward procedure. The discovery of joint points and the creation of explicit rdf links between the data in an automated way can be supported with tools both included in the Interlinking/Fusion LOD2 life cycle. The process that is referred to as interlinking is the main idea behind the Web of Data and leads to the discovery of new knowledge and their combinations in unforeseen ways. Tools such as silk 29  offer a variety of metrics, transformation functions and aggregation operators to determine the similarity of the compared rdf properties or resources. It operates directly on sparql endpoints or rdf files and offers a convenient user interface namely Silk Workbench.
Vocabulary Mapping Sometimes, an enterprise may need to develop a proprietary ontology when applying Linked Data principles. Mapping the terms that were used for publishing the triples with terms in existing vocabularies will facilitate the use of the enterprise data from third-party applications. A tool that supports this kind of mapping is r2r 30. r2r searches the Web for mappings and apply the discovered mappings to translate Web data to the application's target vocabulary. Currently it provides a convenient user interface that facilitates the user in a graphical way to select input data from a sparql endpoint as well as from rdf dumps, create the mappings and write them back to endpoints or rdf files.
Time-Homogeneity The cross-sectional and temporal homogeneity of transition intensities is critical for deriving the modeling approach to transition matrices. A number of past studies investigate transition probabilities functions of distance points in time between different rates and study whether the same transition matrix can be used for each-point-in time (PIT) independently of the date points in time. Some studies conclude that credit rating transitions can be adequately modeled as a Markov chain for typical forecast horizons (one or two years), based on in-sample datasets  [32, 33]. Furthermore, heterogeneity in default probability, measured by a credit rating scoring for instance, is shown to be of critical importance in affecting the shape of the loss distribution  [34, 35]. Several studies specify non-homogeneous models that incorporate the notion of cyclicality and treat credit rating migrations as a non-memory-less process  [35] [36] [37].
Bootstrap Method The bootstrap procedure involves choosing random samples with replacement from a data set and analyzing each sample in the same way, with each observation selected separately in random from the original dataset  [68]. A particular data point from the original data set could appear multiple times in a given bootstrap sample. The number of elements in each bootstrap sample equals the number of elements in the original data set. An important advantage of bootstrap against other methods is that the researcher does not need to have a preconceived notion of the original sample distribution  [42]. A number of past studies  [39, 69]  indicate that 200 and 1000 bootstrap replications are sufficient for obtaining standard errors of bootstrapped statistics and confidence intervals, respectively.
Empirical Application The dataset of the study is based on the entire loan portfolio and internal rating system of a major Austrian bank (anonymity preserved for confidentiality reasons). An initial empirical step relates to defining the segmentation of business sectors; this is based on the respective Standard & Poor's (S&P) segregation, including: financial institutions; insurance; consumer/services; energy and national resources; real estate; leisure time/media; and transportation. Critical issues include also the specification of a rating system with credit quality classes, grades and probabilities of migration from one class to another over the credit risk horizon. Rating agencies, such as Moody's  [70] , Fitch Ratings  [71]  and Standard & Poor's  [72] , for instance, incorporate a 21-, 20-, and a 17-grade scale, respectively, for corporate borrowers.
These borrowers are classified according to their rating history over a 10-year horizon. The rating history of all borrowers (not defaulted by that year-end) in the reference (data-provider) bank's loan portfolio is obtained, as of 31 December 2008. In addition, rating information for all borrowers that defaulted during 1995-2008 is collected. Legal bankruptcy proceedings and loan loss provisions are used as proxies for default. The observation period is eventually restricted to 1998-2008, in order to avoid survivorship bias and to overcome information constraints. The dataset contains initially 210,000 observations on 51,000 borrowers, at quarterly intervals  [17, 58]. Subsequent to a careful data cleansing process, the dataset finally comes down to 185,000 observations on 48,000 firms. A thorough evaluation is normally conducted by the bank once a year (on a year-on-year basis as to the initial obligor's rating).
Statistical analyses Data Normalization: row data (mass spectra) were normalized by log transformation (x-m)/s in order to stabilize the variance between samples  [4, 32, 43]. Hierarchical Clustering Analysis (HCA) was performed with the Hierarchical clustering explorer 3. 5 sofltware (Maryland, USA) on the normalized data. Euclidian distances and average linkages were chosen to measure distance. This method allows samples to be grouped into homogeneous and distinct clusters, without imposing preliminary hypotheses on the data. Principal Component Analysis (PCA) is another unsupervised method with the capacity to reduce the complexity of a multivariate dataset. Its goal is to extrapolate important information and display it as a set of new independent variables called principal components.
Such hypotheses could be supported by the comparison of relative peak intensities measured by FTICR-MS and UHPLC (Figure  2F ). Indeed, relative intensities of FTICR-MS peaks corresponding to the elemental formulas of gallic acid, resveratrol and quercitrin were good matches to the various concentrations measured by UHPLC for these compounds (Figure  2F ). Since such compounds are easily ionized under ESI conditions, Figure  2D  confirms that the entire pool of compounds able to potentially be extracted from grapes is not necessarily found in the resulting wine. The diversity of chemical spaces could be observed in the mass distribution and 2-dimentional van Krevelen diagrams (Figure  3A ) were used to five an initial interpretation of such compilations of assigned elemental formulas. Using a home-compiled database of compounds that have been observed in wines, van Krevelen diagrams provide a representation of the specific contributions of the various phenolics, peptides, polysaccharides, nucleotides and any other classes of compounds present in wines that could be negatively ionized  [4]  (Figure  3A ).
Proposed annotations for the discriminant masses (2010, 2011 and 2012 vintages for skin extracts) could be made using information from the literature and relevant databases. Particularly useful were an in-house database specific to plants and especially to grapes and wine, SciFinder Scholar, and the web server MassTRIX  [51]  (Table  S1 ), which queries several databases (KEGG, Lipid Maps and HMDB). Annotations of peaks in the 2010 and 2012 samples indeed confirmed a relatively high occurrence of pertinent glycosidic structures in the 2012 vintage compared to 2010. It must be noted though that the hypothetical pelargonidin glucoside proposed for the 2012 vintage is an anthocyanin structure that has already been observed in Vitis Vinifera wines  [52] , but not from Pinot noir grapes. Metabolite annotations of the 2011 vintage spectra were characterized by pertinent hypothetical structures including sugars, phenolic acids and flavanols (Table  S1 ).
With such high-resolution mass data, reliable structural assumptions could be drawn by querying topical databases on different annotated Vitis vinifera organism pathways, such as KEGG, accessible with the MassTRIX interface  [51, 55]  (Figure  S2 ). 48 out of the 68 possible wine metabolites identified arise from the flavonoid biosynthesis pathway (see Figure  S2A ). Many of these metabolites are known to exist in wines and are therefore a reliable validation of such database querying using our raw sets of masses  [4]. Applying a similar MassTRIX treatment to must, skin and wine led to the identification of 58, 51 or 48 distinct metabolites from the flavonoid biosynthesis-, fructose and mannose metabolism-, or fatty acid biosynthesis pathways, respectively, as shown in Figure  S2B. As a whole, MassTRIX treatment of compounds that discriminate musts, skins and wines not only illustrates the possibility to consistently propose structural identifications for some of the compounds, but also shows that hits can be found for less than 10% of the discriminant masses when compared to the different Vitis vinifera pathways.
Moreover, structures from existing related databases could be assigned to less than 20% of all of the detected signals, attesting to the magnitude of the structurally unresolved chemistry of wine  [4]. When samples from both villages are considered for a given vintage (2012), terroir discriminations are immediately observed by PCA analysis of corresponding wine chemical spaces (Figure  5A ). The first principal component accounts for the discrimination between wines from the two villages. It must be noted though, that the chemical space variability within GE wines appeared to be significantly higher than that observed for VR wines. Indeed, GE covers a larger vineyard area (2. 29 ha) than VR (1. 81 ha), which could account for the greater diversity amongst the GE samples. Hypothetical annotations of discriminant masses (VR and GE) could be obtained from the literature and relevant databases (Table  S2 ), revealing characteristic structures as diverse as sugars, phenolic acids or fatty acids (Table  S2 ).
Most interestingly, the hierarchical cluster analysis of all the 2012 samples, including musts and skin extracts (Figure  5B ) showed the excellent separation of the classes previously shown in Figure  4 , and terroir discriminations were visible not only in the wine, but also in the grapes, with a stronger effect seen in musts than in skins and wines. Such results may suggest that winemaking processes could lead to some loss of terroir contributions, at least in young wines. It should be noted, that up to 7850 masses were recorded altogether for the different 2012 samples considered in Figure  5B  of which 504 were discriminant for VR differentiation and 207 for GE. However, due to the limitations of current databases, very few relevant masses could be annotated. Based on these results, which show that some terroir impacts could potentially be stronger in grapes than in wines, HCA has been performed for each of the three vintages (Figure  6A ) and PLS-DA analyses (Figure  6B-C-D-E ) of musts and skin extracts for the two villages are presented.
Hence, both tiers are expected to differ in their response to cyclical movements. They are typically also expected to behave differently during periods of growth. Capital accumulation would only be expected in the upper tier, while subsistence activities would be expected to be caught in a poverty trap and stagnate at low levels of capital. Similarly, total factor productivity would be expected to be low and stagnant in the lower tier, with productivity gains being limited to upper tier firms. Based on the recent evidence mentioned above, we think that this distinction of an upper and lower tier is too simplistic and hides the high potential of many firms at the lower end of the capital distribution. Taking this as a background, this paper attempts to answer the following questions: which transformations occurred in the informal sector in the decade under review? In particular, which activities expanded and contracted in terms of employment and capital? Do returns to capital and labor increase or decrease with economic expansion? How do all these changes relate to the macroeconomic dynamics? To answer these questions, we rely on a quite unique dataset of four representative and perfectly comparable cross-sectional surveys of informal enterprises in  Antananarivo in 1995 Antananarivo in , 1998 Antananarivo in , 2001 Antananarivo in and 2004, i.
e, i. e. before and after the 2002 crisis. This dataset allows a detailed description of the characteristics of the informal sector and its firms during a relatively long period of sustained growth, followed by a crisis and two years of recovery. The remainder of the paper is organized as follows. In Section 2, we briefly present the economic and political context in Madagascar over the 1995 to 2005 period. In Section 3 we discuss the theoretical framework and the related empirical literature. In Section 4 we describe the data and main characteristics of the informal firms we analyze. In Section 5 we present our results organized in five sub-sections. We start by analyzing the patterns of firm growth and capital accumulation over time taking into account the heterogeneity of informal firms by systematically disaggregating by sector.
6 They further substantiate their findings by comparing Mexican and US microenterprises. This comparison shows remarkable similarities with respect to the dynamics of self-employment in both countries. Firm performance and dynamics have also recently been studied by  Byiers & Iacovone (2011) , using cross-country data and firm-level data collected in 2004 and 2007 in Madagascar. They first run a cross-country regression to investigate firm growth determinants using a pooled firm level dataset from 11 developing countries including India, Vietnam, Mauritius and several sub-Saharan African countries. They find a positive relationship between firm size growth and firm size. When using a panel of 135 Malagasy formal firms, they find a negative effect of firm age on growth. In the same study they also compare labor productivity in small informal firms with labor productivity in small formal firms.
1-2-3 Surveys We use data from the 1-2-3 Surveys, collected in Antananarivo between 1995 and 2004  (Rakotomanana, et al. , 2003; Razafindrakoto, et al. , 2009). These surveys were specifically designed to collect information among representative samples of informal firms. Phase 1 is a labor force survey, conducted every year since 1995, among 3,000 households. Principal and secondary activities of every member aged 10 years and over are recorded, including the type and status of the firm (formal/informal). This allows establishing a list of all informal firms headed by any member of a household (whether it is the main or the secondary activity). This serves as the sampling frame for phase 2, in which around 1,000 firms are surveyed. These have been randomly selected from the phase 1 listing.
Hence, we can use data on a representative sample of approximately 4,000 informal firms surveyed in  1995, 1998, 2001 or 2004. Four key advantages of the 1-2-3 survey over other alternative datasets need to be stressed here. First, the mixed household-enterprise survey frame is the only one to ensure the full representativeness of the informal sector. Previous studies, especially in Africa, are typically based on enterprise surveys (like 'Regional Program on Enterprise Development' surveys) which cover only a part of all informal firms; in general the upper tier of the informal sector in some specific industries, mainly manufacturing. Second, our four rounds of surveys are fully comparable, as sampling methodology and questionnaire have been maintained constant over time. To our knowledge, this is the first time ever such a series of repeated cross sections on informal firms on a representative basis is available in Sub-Saharan Africa.
This research is part of a project entitled " Unlocking potential: Tackling economic, institutional and social constraints of informal entrepreneurship in Sub-Saharan Africa " (http://www. iss. nl/informality)funded by the  Austrian, German, Norwegian, Korean and Swiss Government  through the  World Bank's Multi Donor Trust Fund  Project: " Labor Markets ,  Job Creation , and Economic Growth, Scaling up Research, Capacity Building, and  Action  on the Ground". The financial support is gratefully acknowledged. We thank participants at the CSAE 2011 Annual Conference in Oxford, at the  Development Economics Conference 2011 of the German Economic Association in Berlin  and at the  DIAL Development Conference.