dataesr/scientific-paragraphs-categorization huggingface dataset:
Although the HYDE land-use-change data set indicates whether landuse changes occur on forested or non-forested land, typically only the changes in agricultural areas are used by the models and are implemented differently within each model (e. g. an increased cropland fraction in a grid cell can either be at the expense of grassland, or forest, the latter resulting in deforestation; land-cover fractions of the non-agricultural land differ between models). Thus the DGVM forest area and forest area change over time is not consistent with the Forest Resource Assessment of the FAO forest area data used for the bookkeeping model to calculate E LUC. Similarly, model-specific assumptions are applied to convert deforested biomass or deforested area, and other forest product pools, into carbon in some models (Table  5 ).
The decadal change is computed from the difference in concentration 10 years apart based on a measurement error of 0. 35 ppm. This error is based on offsets between NOAA/ESRL measurements and those of the World Meteorological Organization World Data Centre for Greenhouse Gases (NOAA/ESRL, 2015a) for the start and end points (the decadal change uncertainty is 2(0. 35 ppm) 2 (10 yr) -1 assuming that each yearly mea-surement error is independent). This uncertainty is also used in Table  8. The contribution of anthropogenic CO and CH 4 is neglected from the global carbon budget (see Sect. 2. 7. 1). We assign a high confidence to the annual estimates of G ATM because they are based on direct measurements from multiple and consistent instruments and stations distributed around the world  (Ballantyne et al.
, 2014 Bakker et al. , , 2015)) , which contains 14. 5 million data to the end of 2014. This was extended with 1. 4 million additional measurements over years 2013-2014 (see data attribution Table A1 in Appendix A), submitted to SOCAT but not yet fully quality controlled following standard SOCAT procedures. Revisions and corrections to previously reported measurements were also included where they were available. All new data were subjected to an automated quality control system to detect and remove the most obvious errors (e. g. incorrect reporting of metadata such as position, wrong units, clearly unrealistic data). The combined SOCAT v3 and preliminary new 2013-2014 measurements were mapped using a data-driven diagnostic method  (Rödenbeck et al. , 2013)  and a combined self-organising map and feed-forward neural network  (Landschützer et al.
Discussion Each year when the global carbon budget is published, each component for all previous years is updated to take into account corrections that are the result of further scrutiny and verification of the underlying data in the primary input data sets. The updates have generally been relatively small and focused on the most recent years, except for land-use change, where they are more significant but still generally within the provided uncertainty range (Fig. 9 ). The difficulty in accessing land-cover-change data to estimate E LUC is the key problem to providing continuous records of emissions in this sector. Current FAO estimates are based on statistics reported at the country level and are not spatially explicit. Advances in satellite recovery of land-cover change could help to keep track of land-use change through time  (Achard et al.
, 2012 Canadell et al. , -2013)). Annual estimates of each component of the global carbon budgets have their limitations, some of which could be improved with better data and/or better understanding of carbon dynamics. The primary limitations involve resolving fluxes on annual timescales and providing updated estimates for recent years for which data-based estimates are not yet available or only beginning to emerge. Of the various terms in the global budget, only the burning of fossil fuels and atmospheric growth rate terms are based primarily on empirical inputs supporting annual estimates in this carbon budget. The data on fossil fuels and industry are based on sur- vey data in all countries. The other terms can be provided on an annual basis only through the use of models. While these models represent the current state of the art, they provide only simulated changes in primary carbon budget components.
This all requires more frequent, robust, and transparent data sets and methods that can be scrutinised and replicated. After 10 annual releases from the GCP, the effort is growing and the traceability of the methods has become increasingly complex. Here, we have documented in detail the data sets and methods used to compile the annual updates of the global carbon budget, explained the rationale for the choices made, the limitations of the information, and finally highlighted the need for additional information where gaps exist. This paper will help, via "living data", to keep track of new budget updates. The evolution over time of the carbon budget is now a key indicator of the anthropogenic perturbation of the climate system, and its annual delivery joins a set of other climate indicators to monitor the evolution of human-induced climate change, such as the annual updates on the global surface temperature, sea level rise, and minimum Arctic sea ice extent.
The naming convention of the budgets has changed. Up to and including 2010, the budget year (Carbon Budget 2010) represented the latest year of the data. From 2012, the budget year (Carbon Budget 2012) refers to the initial publication year. b The CDIAC database has about 250 countries, but we show data for about 216 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2. 1. 1 for more details).
We thank  W. Peters  for CTE2015 model simulations, and all data providers to ObsPack GLOBALVIEWplus v1. 0 for atmospheric CO 2 observations. NERC  provided funding to  C. Le Quéré ,  R. Moriarty , and the GCP through their  International Opportunities Fund specifically  to support this publication ( NE/103002X/1 ). G. P. Peters  and  R. M. Andrew  were supported by the  Norwegian Research Council  ( 236296 ). J. G. Canadell  was supported by the  Australian Climate Change Science Programme. S. Sitch  was supported by  EU   FP7  for funding through projects  LUC4C  ( GA603542 ). R. J. Andres was supported by  US Department of Energy, Office of Science, Biological and Environmental Research (BER)  programmes under  US Department of Energy  contract  DE-AC05-00OR22725. T. A. Boden  was supported by  US Department of Energy, Office of Science, Biological and Environmental Research (BER)  programmes under  US Department of Energy  contract  DE-AC05-00OR22725.
J. I. House  was supported by the  Leverhulme foundation  and the  EU   FP7  through project  LUC4C  ( GA603542 ). P. Friedlingstein was supported by the  EU   FP7  for funding through projects  LUC4C  ( GA603542 ) and EMBRACE (GA282672). A. Arneth was supported by the  EU   FP7  for funding through  LUC4C  ( 603542 ), and the  Helmholtz foundation and its ATMO programme. D. C. E. Bakker  was supported by the  EU   FP7  for funding through project  CARBOCHANGE  ( 284879 ), the  UK Ocean Acidification Research Programme  ( NE/H017046/1 ; funded by the  Natural Environment Research Council , the  Department for Energy and Climate Change  and the  Department for Environment, Food  and Rural Affairs). L. Barbero was supported by  NOAA's Ocean Acidification Program  and acknowledges  C. Le Quéré  et al.
: Global Carbon Budget 2015 support for this work from the  National Aeronautics and Space Administration (NASA) ROSES Carbon Cycle Science  under  NASA  grant  13-CARBON13_2-0080. P. Ciais  acknowledges support from the  European Research Council  through Synergy grant  ERC-2013-SyG-610028  " IMBALANCE-P ". M. Fader was supported by the  EU   FP7  for funding through project  LUC4C  ( GA603542 ). J. Hauck  was supported by the  Helmholtz Postdoc Programme (Initiative and Networking Fund of the Helmholtz Association). R. A. Feely  and  A. J. Sutton  were supported by the  Climate Observation Division, Climate Program Office ,  NOAA ,  US Department of Commerce. A. K. Jain was supported by the  US National Science Foundation  ( NSF AGS 12-43071 ) the  US Department of Energy, Office of Science and BER programmes  ( DOE DE-SC0006706 ) and  NASA   LCLUC programme  ( NASA NNX14AD94G ).
E. Kato was supported by the  ERTDF (S-10)  from the  Ministry of Environment, Japan. K. Klein Goldewijk  was supported by the  Dutch NWO VENI  grant no. 863. 14. 022. S. K. Lauvset  was supported by the project " Monitoring ocean acidification in Norwegian  waters" from the  Norwegian Ministry of Climate and Environment. V. Kitidis  was supported by the  EU   FP7  for funding through project  CARBOCHANGE  ( 264879 ). C. Koven was supported by the Director,  Office of Science, Office of Biological and Environmental Research of the US Department of Energy  under contract no. DE-AC02-05CH11231  as part of their Regional and  Global Climate Modeling Program. P. Landschützer was supported by  GEOCarbon. I. T. van der Lann-Luijkx  received financial support from  OCW/NWO  for  ICOS-NL  and computing time from  NWO  ( SH-060-13 ).
B. Pfeil was supported through the  European Union 's  Horizon 2020 research and innovation programme AtlantOS  under grant agreement no. 633211. D. Pierrot was supported by  NOAA  through the  Climate Observation Division of the Climate Program Office. B. Poulter was supported by the  EU   FP7  for funding through GEOCarbon. G. Rehder was supported by  BMBF  ( Bundesministerium für Bildung und Forschung)  through project  ICOS , grant no. 01LK1224D. U. Schuster  was supported by  NERC UKOARP  ( NE/H017046/1 ),  NERC RAGANRoCC  ( NE/K002473/1 ), the  European Space Agency (ESA) OceanFlux Evolution project , and  EU FP7 CARBOCHANGE (264879 ). T. Steinhoff was supported by  ICOS-D  ( BMBF FK 01LK1101C ) and  EU   FP7  for funding through project  CARBOCHANGE  ( 264879 ). J. Schwinger was supported by the  Research Council of Norway  through project  EVA  ( 229771 ), and acknowledges the  Norwegian Metacenter for Computational Science (NOTUR , project  nn2980k ), and the  Norwegian Storage Infrastructure  ( NorStore, project ns2980k ) for supercomputer time and storage resources.
T. Takahashi was supported by grants from  NOAA  and the  Comer Education and Science Foundation. B. Tilbrook was supported by the  Australian Department of Environment  and the Integrated Marine Observing System. B. D. Stocker was supported by the  Swiss National Science Foundation  and  FP7  funding through project  EMBRACE (282672 ). S. van Heuven was supported by the  EU   FP7  for funding through project  CARBOCHANGE  ( 264879 ). G. R. van der Werf was supported by the  European Research Council  ( 280061 ). A. Wiltshire was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) and  EU   FP7 Funding  through project  LUC4C (603542 ). S. Zaehle was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (QUINCY ; grant agreement no.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights of how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data are given at the top of each page in the accompanying database, and summarised in.
The naming convention of the budgets has changed. Up to and including 2010, the budget year (Carbon Budget 2010) represented the latest year of the data. From 2012, the budget year (Carbon Budget 2012) refers to the initial publication year. b The CDIAC database has about 250 countries, but we show data for about 216 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2. 1. 1 for more details). a (this study) 2015 Le Quéré et al. (2015) 2014 Le Quéré et al. (2014) 2013 Peters et al. (2013) Le Quéré et al. (2013) 2012 Peters et al. (2012b) 2011 Friedlingstein et al. (2010) 2010 Le Quéré et al. (2009) 2009 2008 (online) Canadell et al. (2007) 2007 Raupach et al. (2007) 2006 Publication year a data Three years of BP GDP rent year based on Projection for cur- Global with CDIAC) provided (along tended to 2014 also from UNFCCC ex- National emissions data Three years of BP 250 countries b 1959 129 countries from emitters Emissions for top Annex B nex B and non- Split between An- Split in regions (territorial) Country Fossil fuel emissions based on GTAP9 introduced for 2011 Detailed estimates data with updated GDP Extended to 2012 and 2007 1997, 2001, 2004, timates for years with detailed es- based on GTAP8.
New and different information sources have appeared over the past years (e. g. Blogs, Media, Open Data, Scientific Data and Social Networks). The variety of these sources is growing and the related data volume increases exponentially. Open Data (OD) initiatives and platforms are one of the current major data producers, also because the topic seems to be important for many governments world-wide. Given the many fields and sectors involved, OD brings high business and societal potential. The amount and diversity of available information is high. However, analysing and understanding OD in order to exploit is far from being an easy task. Several problems and constraints must be solved. Information Visualization (InfoVis) can help to give a graphical idea of the processed files structure. Given that OD is provided very often as tabular data, this paper focuses on OD CSV files.
INTRODUCTION The amount of information being generated and released every day on the Internet is enormous. This fact results mainly from the growing number of new information sources: Open Data (OD), Social Networks and Media (e. g. Twitter, Facebook), blogs, scientific data, commercial data, and so on. OD is a current active movement in terms of data generation and release. This is mainly due to the pressure made by governments in order that data produced and maintained by public entities should be accessible to the global public. The economic value of OD has been estimated at 40 billion, per year, in Europe alone  (Euro-peanCommission, 2014). At the same time, OD represents an important element of the way towards more IT supported participation and transparency  (Janssen, 2011)  in the public sector.
Many actors of both the private and the public sector are involved in OD publication: agriculture, economy, health, culture, transports, education, etc. This diversity is one of the factors that explain why OD has a high value and an important potential for the society and the economy in general. Every data, and in particular, every OD dataset has its own business/social value. To realize the maximum potential of datasets, however, it may be necessary to combine them. We have been exploring this area in order to create adequate charts to sup-port OD integration processes. However, before combining data, it is necessary to understand it. One of the major problems concerning OD is the lack of a common and unique standard used by organizations to publish their datasets. This leads to datasets being published and accessible in different formats and shapes: files (in different formats), RSS, APIs, etc.
Understanding how datasets are organized is a major issue considering all kinds of dataset formats that may exist. This problem is known as the "table recognition problem"  (Ng et al. , 1999). It consists in identifying, in a given data file, the important parts of data tables such as headers (columns and rows), data values and their types  (string, numeric, etc) , missing values and other information in the file. Some files might contain more than one table. Once the table structure is understood, the table can be further processed and combined with other information: it can be integrated in a data warehouse, or it can be processed by a data mining or a visualization tool. This paper focuses on OD CSV files. The reason of this choice is explained in a later section. We support the idea that Information Visualization is a promising candidate to ease the understanding of OD CSV files structure.
The paper tries to answer the challenge of presenting an intuitive and efficient manner to visualize the structure of such datasets. The rest of the paper is structured as follows: First, we give a brief introduction of table structure analysis, followed by an overview on the state of the art in this field. Several chart types we have worked on during our analysis are presented and their advantages and limitations are discussed. Our chosen chart solution -Piled Chart -is described in detail. Finally, we present the results and conclusions of using Information Visualization to support CSV files structure analysis.
CSV Format Several initiatives encourage the use of machinereadable formats for OD. For the prevailing tabular datasets, the simplest and widest-spread format is CSV (comma-separated values). Its usage in OD contexts is commonplace. The Open Government Partnership (OGP) was launched in 2011  (Harrison et al. , 2012)  to stimulate the creation of open governments worldwide. Currently, OGP has 64 participating countries  (OpenGovernmentPartnership, 2014). Members of this organization are required to respect various open-governance standards  (Yu and Robinson, 2012) , concerning e. g. the type of information released or the format of the published data. In general, datasets are required to be released in a machinereadable format. In Europe, the Financial Transparency System (FTS) of the European Commission provides information related with European Union projects since the year of 2007  (Martin et al.
, 2014). All the datasets can be downloaded as CSV files. In the Netherlands, Zuiderwijk and Janssen made a study of the OD policy of seven countries  (Zuiderwijk and Janssen, 2014). It shows that standard formats, including CSV, are used most of the time. In 2014, Veljkovi et al. presented a benchmark proposal regarding OD available in the United States OD portal  (Data. Gov, 2012). In this study, it has been concluded that most of the OD datasets were available in CSV, XLS and PDF files  (Veljković et al. , 2014). Finally, a recent study of OD policies in five different countries (United States, United Kingdom, Netherlands, Kenya and Indonesia) has confirmed that CSV is used in all involved countries except Indonesia, where datasets are only available as PDF files  (Nugroho, 2013). All these facts demonstrate the importance of the CSV format for OD, and why we focus our research on CSV files.
This choice is also linked to the bellow reasons: • CSV is a machine-readable format; • CSV is a basic, non-proprietary format; • CSV is a simple tabular format. However, the advantage of the extreme simplicity of the format is not without issues. The semantic and syntactic interpretation of CSV files can be difficult. Furthermore, there is no common standard used for publishing OD  (Rivero et al. , 2012). In order to profit from the presumptive high potential businessvalue of OD, data must be made usable, meaningful and exploitable. Getting an overview of the structure and the content of a CSV file can be a hard task. Despite its popularity, this format does not have a formal specification. The informal de-facto standard is RFC 4180  (Shafranovich, 2005)  that specifies the following syntatic rules for CSV files: • Records are located on a separated line delimited by a line break; • Last record of the file may not have an ending line break; • An optional header line may exist on the first line of the file; • Within the header and each record, there may be one or more fields separated by commas; • Each field may or not be enclosed in double quotes; • Fields containing line breaks, double quotes and commas should be enclosed in double-quotes; • In fields where double-quotes are used to enclose them, a double-quote must precede another one appearing inside a field to escape it.
IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications the meaning of large tables using a fisheye approach. The idea behind this methodology is based on a distortion applied by the user where the centre of the distortion becomes zoomed-in while the other regions displayed are zoomed-out  (Sundararajan et al. , 2011). (Sopan et al. , 2010). However, data types were also not taken into account. Although its global utility, these methods are not convenient regarding the type of CSV structure analysis we want to achieve. We support the idea that it is necessary to create a more specific solution to assess the structure of a CSV file. Our objective is to provide the user with an intuitive and global overview of the structure of a CSV file and the type of data it contains, in order to determine what pre-processing must be performed on such a table.
Our aim is to create a visualization technique that is able to give a global overview of an OD CSV file structure: • To estimate the size (number of rows/columns) of the CSV file; • To provide information on the data type of CSV file cells; • The possibility to compare the same type of datasets over time periods. This can be very useful to detect problems, errors and incoherencies because OD datasets are often published periodically. Additionally, the new chart should have the following characteristics: • Be efficient -in order to deliver a rapid idea of the CSV file form. Users with limited or even poor knowledge about the analysed information should be capable of getting a global idea of the CSV file structure in an effective and intuitive manner; • Be user-friendly provinding e. g. zoom-in/zoomout functions; legends; tooltip information; etc.
CSV Cell Chart Succeeding previous chart solutions, CSV Cell Chart was created (Figure  3 ). CSV Cell Chart was a step forward to our solution -Piled Chart is completely based on this approach. CSV Cell Chart consists merely in showing every column, row and cell in the same order as they are in the CSV file. In other words, it gives to the user an image of the CSV file structure, using a colour code to represent the data type of each cell. This colour code is also used to show possible warnings/errors.
Piled Chart CSV Cell Chart has an important weakness: it displays all rows/columns, even if they have the same structure. This fact is not optimal in terms of space usage and of the visualization. To eliminate this inconvenience, retaining however the advantages of the CSV Cell Chart, the Piled Chart was created. Its main characteristics are: • Rows with the same structure -rows with the same data types on the same column index -are grouped (piled) into an unique row; • Columns with the same structure -columns with the same data types on the same row index -are grouped (piled) into an unique column; • A colour code is used to represent the data type for every cell. For now the data types supported are limited (String, Number, Date and Percentage). The colour code may also be used to represent possible errors and/or warnings.